Over the past decades, massive increases in the scale and types of annotated or labeled data have accelerated advances in all areas of machine learning. This has enabled major advances is many areas of science and technology, as complex models of physical phenomena or user behavior, with millions or perhaps billions of parameters, can be fit to datasets of increasing size. For example, in computer vision, machine learning models traditionally rely on training datasets comprising a large number of feature-labeled images. However, manually labeling these training images requires considerable human resources, particularly when there are areas of the images that may be difficult for humans to label (e.g., poor quality areas, obscured areas, etc.). Accordingly, service providers face significant technical challenges to enable efficient labeling of images given limited human resources.